We implement \(\mathsf {Starmie}\)  in Python using Pytorch and the Hugging Face Transformers library [1]}.
For contrastive learning, we use RoBERTa [2]} as the base language model.
We set the hyper-parameters batch size to 64, learning rate to 5e-5, and max sequence length to 256 across all the experiments.
All experiments are run on a server with configurations similar to those of a p4d.24xlarge AWS EC2 machine with 8 A100 GPUs.
The server has 2 AMD EPYC 7702 64-Core processors and 1TB RAM.
